Do not start by telling the testers to go faster. First work out what they are spending the time on.
Slow testing is often a symptom. Broken environments, poor test data, late builds, enormous regression packs, duplicated approval steps, flaky automation and unclear scope can make a perfectly competent team look painfully slow.
If you are being told testing is the bottleneck, it is worth finding out whether testing is actually slow or whether the rest of delivery has simply dumped its delays there.
Follow one change from build to release
Pick a normal change and write down when it was genuinely being tested and when it was waiting. Waiting for deployment. Waiting for data. Waiting for access. Waiting for a defect fix. Waiting for a supplier. Waiting for somebody to approve the next step.
You may discover the ‘testing delay’ is three hours of testing wrapped in four days of organisational faffing. Asking the tester to type faster will not fix Tuesday.
Cut work that no longer earns its keep
Review the regression pack. Remove duplicates. Stop manually repeating stable low-risk checks that are already covered elsewhere. Move useful automated checks earlier. Fix flaky automation rather than rerunning it until it becomes emotionally ready to pass.
Also ask whether every release needs the same depth. A tiny content change and a payment-engine rewrite should not trigger identical ceremonies just because the process diagram has one lane.
Measure useful testing time before trying to speed it up
For one ordinary release, ask the test team to log broad categories of time for three days: testing, investigating defects, waiting for builds, waiting for data, retesting and producing governance updates. Do this to diagnose the system, not to grade individual testers.
Suppose the team spends 18 hours running useful checks, 12 hours waiting for deploys, 8 hours recreating test data and 6 hours preparing status. Automating a two-minute test is not your first opportunity. Stabilising deployment and data could return 20 hours before anyone touches the regression suite.
Choose one intervention, such as reliable data refresh before the test window, and compare waiting time on the next release. If the actual constraint sits across the whole programme rather than inside the test service, use an end-to-end flow analysis instead: find the real programme bottleneck.
Fix the thing causing the wait
If deployment takes six hours, improve deployment. If test data takes two days, fix data provisioning. If the environment falls over every afternoon, stop describing that as tester productivity. If defects sit for a day before anybody looks at them, improve defect turnaround.
This sounds obvious, yet organisations regularly launch a ‘test efficiency initiative’ that trims defect descriptions and introduces a new dashboard while the environment still spends half the week unavailable.
Faster feedback usually comes from improving the delivery system, not motivating people harder. Testing can absolutely be inefficient, but prove where the time goes before you optimise the wrong thing.
Be careful with automation promises too. Automating a slow manual check can help, but it does nothing for a build that arrives late, a database nobody can refresh or a queue of defects waiting for developers. Automate the work that is genuinely repetitive after you have removed the waiting around it.
If testing is the bottleneck and nobody can agree why, get onto us. We can trace the actual flow, separate testing time from waiting time and recommend what to automate, simplify, remove or fix. Sometimes the best way to speed up testing is to stop blaming testing for everything around it.